Best Data Formats Supported in Snowflake

Snowflake offers a comprehensive catalog of file format options that enable you to tailor your data files according to the type and formatting of the information they contain. Allowing for maximum customizability, Snowflake’s set defaults make loading any sort of data as seamless as possible.

There are six formats that are best for loading data into Snowflake, including:

1. Tabular Data

In Snowflake, tabular data refers to data that is organized into rows and columns, similar to a spreadsheet. Tabular data can be stored in various formats, including flat files (such as CSV or JSON) and more specialized formats like Parquet.

To load tabular data into Snowflake, you can use the COPY INTO command. This command allows you to load data from a file or other external source into a table in Snowflake. 

The COPY INTO command has various options that allow you to specify the format of the data being loaded, the target table, and other details of the load operation. You can also use the command to load data from various external sources, such as Amazon S3, Microsoft Azure, or Google Cloud Storage.

Once the data is loaded into Snowflake, you can use SQL queries to analyze and manipulate the data. Snowflake’s SQL syntax is similar to other database systems, so if you are familiar with SQL you should be able to start working with the data quickly.

2. JSON

JSON (JavaScript Object Notation) is a lightweight data-interchange format that is used to store and transmit data structures. JSON is often used to transmit data over the web and is supported by many APIs and other web-based services.

JSON files are preferred when loading data into Snowflake as they require minimal pre-processing and allow for the flexibility of dynamic schemas.

It can be produced by any application, including:

• Apps using native JSON web services
• Non-JS-based applications like PHP and Ruby that use libraries to parse and generate JSON files
• On-demand JavaScript generators like Node.js
• Concatenation of multiple JSON files

As there is no approved formula for the construction of JSON-like data sets, discrepancies between various implementations can make it difficult to import them. To enable easier and smoother imports, Snowflake has adopted a “be liberal in what you accept” rule to maximize its capacity to process diverse inputs with clear meaning.

For more, visit http://www.json.org/

3. Parquet

Apache Parquet is an open-source columnar storage format designed for Hadoop and optimized for fast analytics on big data. It stores data in columns to reduce I/O operations, which makes it more efficient than row-based formats.

Parquet files provide greater data compression and are ideal for loading large volumes of structured data into Snowflake.

Snowflake effortlessly reads Parquet data and stores it in a VARIANT column, allowing you to query the data with similar commands as JSON. If needed, an alternative is extracting chosen columns from your staged Parquet file into individual table columns using CREATE TABLE AS SELECT statements.

For more, visit https://parquet.apache.org/docs/

4. ORC

Optimized Row Columnar (ORC) is an open-source file format that stores data more efficiently than traditional row-oriented files like CSV or TSV. ORC files are more compact, which makes them more suitable for large-scale data loading into Snowflake.

ORC, or Optimized Row Columnar, is an ingeniously designed binary format developed to enhance Hive data storage. It’s structured for both efficient compression and improved read/write performance in comparison with traditional Hive file formats.

Snowflake cleverly loads ORC data into a single VARIANT column, enabling you to query it in the same manner as JSON data with similar functions and commands.

You can easily extract selected columns from an existing ORC file and place them into individual table columns via a CREATE TABLE AS SELECT statement.

For more information, visit https://orc.apache.org/.

5. Avro

Avro, an open-source data serialization and RPC framework created for Apache Hadoop applications, uses JSON schemas to generate a compact binary format of your data. This allows the deserialized information from any destination (program or application) to remain intact since it is accompanied by its schema. Thus, Avro facilitates seamless transmission between sources and destinations.

An Avro schema is a JSON string, object, or array that defines the type of data structure and its attributes. Attributes can range from field names to data types depending on whether it’s a simple or complex schema. Complex structures such as arrays and maps are supported for use in an Avro schema, allowing you to create comprehensive schemas with ease.

Snowflake converts Avro data into a single VARIANT column that can be readily queried using the same commands and functions as for JSON data.

Avro files are best for loading semi-structured data into Snowflake, as it provides better space utilization. It also has a direct mapping to and from JSON, which makes it easier to exchange data across systems.

For more, visit http://avro.apache.org/

6. XML

XML (eXtensible Markup Language) is a standardized, markup language that is used to encode data in a human-readable and machine-readable format. In Snowflake, XML data is stored as a variant data type, which can store data in a number of different formats, including text, numbers, and binary data.

One of the main advantages of using XML as a data format in Snowflake is that it allows you to store complex, hierarchical data structures in a single column. This can be useful if you have data that has a lot of relationships and dependencies between different elements.

To work with XML data in Snowflake, you can use several built-in functions, such as XMLPARSE, XMLEXISTS, and XMLQUERY, to extract and manipulate data from the XML documents. You can also use these functions to join XML data with other data in your Snowflake tables.